Publications by Jake Reynolds - August 23, 2020
Final Presentation / Paper
Load Some Packages to Help with the Analysis and Data Management: library(Hmisc) suppressPackageStartupMessages(library(psych)) suppressPackageStartupMessages(library(tidyverse)) suppressPackageStartupMessages(library(lme4)) Load and Explore the Data projectCHPS <- readr::read_csv("ColonialHeightsMAPReadingScores2020.csv") -- Column specifi...
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Module 11 - Cross-classified Linear Mixed Models
Part 1: Running Separate and Combined Null Models 1.Using reading score (p7read) as the outcome, run and interpret three separate null models, one for school (schid), one for neighborhood (neighid), and one additive null model combining school and neighborhood. The AIC and BIC for the three null models are as follows: school = 18558.8 and 18576.0...
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Module 9
Treating “readprof” as the DV, and “schcode” as the level-2 clustering variable, estimate a null model. Compute, report, and interpret the ICC. Is it worth conducting MLM on these data? The ICC is 0.06489173. Since it is above 0.05, then we would move forward with MLM. Interpret the estimated fixed effect of the intercept (_cons) in the...
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Module 10 - Checking Assumptions for MLM
Part 1: Running and Our “Final” 2-Level Model Run a conditional random intercept model with math scores (gktmathss) as the DV, and self-concept (gkselfconcraw) as a student-level IV, and classroom type (gkclasstype) and teacher highest degree (gkthighdegree) as teacher/classroom-level IVs. Save these model results for later. Part 2: Check T...
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Multivariate Stats - Module 1
library(tidyverse) library(psych) library(haven) gss <-read_dta("descriptive_gss.dta") View(gss) glimpse(gss) Rows: 2,765 Columns: 16 $ id <dbl> 2331, 2003, 1221, 2051, 2465, 546, 1291, 732, 303, 2700, 855, 62... $ hrs1 <dbl+lbl> NA, NA, NA, NA, 50, 60, 40, 25, NA, 40, 64, 45, 60, 85, NA, ... $ marital <dbl+lbl> 1, 3, 1, 1, 1,...
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Multivariate Statistics - Module 2
library(tidyverse) library(Hmisc) library(haven) gss <- read_dta("gss_2002_week2.dta") View(gss) gss.clean <- gss %>% mutate(., male.fac = as_factor(male), selfemp.fac = as_factor(selfemp), ses_level.fac = as_factor(selfemp)) linearmodel1 <- lm(hrs1 ~ male + age + selfemp + educ, data = gss.clean) summary(l...
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Content Review 4 - MVS
Example #1: ANCOVA with an experimental design and pretest as covariate Load in some helpful packages library(tidyverse) library(haven) Load in the dataset write_ancova <- read_dta("Writing_Tech.dta") Explore your data glimpse(write_ancova) Rows: 30 Columns: 3 $ Treatment <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,... ...
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Module 3 - Multivariate Statistics
Load in relevant packages library(tidyverse) Registered S3 methods overwritten by 'dbplyr': method from print.tbl_lazy print.tbl_sql -- Attaching packages --------------------------------------- tidyverse 1.3.0 -- v ggplot2 3.3.2 v purrr 0.3.4 v tibble 3.0.3 v dplyr 1.0.2 v tidyr 1.1.1 v stringr ...
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Module 4 - take 2
Example #1: ANCOVA with an experimental design and pretest as covariate Load in some helpful packages library(tidyverse) library(haven) Load in the dataset write_ancova <- read_dta("Writing_Tech.dta") Explore your data glimpse(write_ancova) Rows: 30 Columns: 3 $ Treatment <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3...
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Final Project - Spring 2021
library(tidyverse) library(corrr) library(psych) library(haven) sel <- read_csv("35sel2.csv") Parsed with column specification: cols( .default = col_double(), submitted = col_character() ) See spec(...) for full column specifications. Explore your data with glimpse describe(sel) str(sel) tibble [382 x 37] (S3: spec_tbl_df/tbl_df/tbl...
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